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Recurrence Networks in Natural Languages.

Edgar Baeza-Blancas1,2, Bibiana Obregón-Quintana3, Candelario Hernández-Gómez1

  • 1Departamento de Física, Escuela Superior de Física y Matemáticas, Ciudad de México 07738, Mexico.

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Summary
This summary is machine-generated.

This study uses recurrence networks to analyze character patterns in 17 European languages. Results reveal linguistic family similarities in network density, aiding language classification.

Keywords:
natural languagespatterns repetitionrecurrence networks

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Area of Science:

  • Computational linguistics
  • Network science
  • Natural language processing

Background:

  • Understanding the structural properties of written texts across languages is crucial for linguistic analysis.
  • Recurrence network methods offer a novel approach to quantify textual patterns without relying on traditional linguistic units like words.

Purpose of the Study:

  • To investigate the application of recurrence network analysis to identify structural similarities and differences in written texts across various European languages.
  • To explore whether network metrics derived from character patterns can reveal underlying linguistic family relationships.

Main Methods:

  • A dataset of 85 ebooks in 17 European languages was analyzed.
  • Character patterns of length 'm' were compared using Hamming distance with a threshold 'r' to identify repetitions.
  • Recurrence networks were constructed, and metrics such as density, clustering, assortativity, and closeness were calculated.

Main Results:

  • Network density, clustering, and assortativity were significantly higher in original texts compared to shuffled sequences.
  • Closeness centrality showed similar values for both original and shuffled sequences.
  • Languages belonging to the same linguistic family exhibited similar average network density values.
  • Linear discriminant analysis successfully clustered languages based on network density, separating different language families.

Conclusions:

  • Recurrence network analysis effectively captures structural properties of written texts, distinguishing between languages and revealing linguistic family affiliations.
  • Character-level pattern analysis using recurrence networks provides a valuable tool for computational linguistics and language classification.